My friend made this - Codebunk.com

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CodeBunk is a Collaborative Editor with an Online Compiler/Interpreter for PHP, Python, Ruby, Perl, Lua, Javascript, C, C++. CodeBunk also has Peer-to-Peer Video/Audio chat facility. CodeBunk is ideal for Online Interviewing of Developers. Current practice Online interviews are the norm in the tech industry given its nature—a handful of hubs looking for brilliant minds from all over the globe. Usually, the process involves 3-4 rounds: The interviewer calls up the interviewee on Skype or on telephone Using a tool like Collabedit or Google Docs, the interviewer creates a document and sends the link to the interviewee, after which the interviewer poses a problem and the interviewee codes it up on the editor The interviewer runs the code on a machine at his/her end or does a dry run (goes through the code mentally to determine its correctness) Why CodeBunk CodeBunk provides one platform for the interviewing process: communication tools (mic and camera), editor and compiler are built in the same interface. It is simple and easy to use. CodeBunk can also be used for learning programming from friends or sharing ones cool algorithms with others (I shared a few right here on HN). CodeBunk is under active development even as I write this post. Lots of features are to be added to make the experience smooth and truly kickass. Do check it out and tell us what you think. You would require to login via Github, Twitter, or Facebook to create Bunks or Fork a Bunk. However, you can view Bunks made by other people without signing up. For eg. http://codebunk.com/bunk#-IsrtQcv125Udslem2wL Sign up and start creating Bunks here - http://codebunk.com P.S. If you are a facebook person, visit their FB page https://www.facebook.com/codebnk. Quora users can visit http://www.quora.com/CodeBunk

Visit product View on Hacker NewsMay 15, 2013

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

83%83% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsStrong engagement from HN community · Strong signals: lua, ide, io · Missing: https docs, excited, just released

78%78% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntOn track for Day 1 leaderboard · Strong signals: mac, google, user · Missing: agents, macos, agent

64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: platform, interface, users · Missing: plus, intuitive, reviews

58%58% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: video, google, users · Missing: mobile apps, ios, personal

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat, audio · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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